NBA All-Star Prediction Using Twitter Sentiment Analysis

Autor:	Yue-Qun Chen, Yi-Jen Su
Rok vydání:	2014
Předmět:	Information retrieval Computer science media_common.quotation_subject Voting Star (game theory) Sentiment analysis Quality (business) Social media Noise (video) Filter (software) Social network analysis media_common
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783319017952 ICGEC
DOI:	10.1007/978-3-319-01796-9_20
Popis:	As Web 2.0 services become more popular, social network analysis related research have received more attention. Typically, most Internet users contact others through a variety of social media, such as Facebook or Twitter. This research explores human behavior by conducting opinion mining on Twitter to predict the final voting results of NBA All-Star 2013. The term-feature model is proposed to filter out noise for enhancing the quality of the tweet corpus. Tweenator, an emotion detector, assists to decide whether the emotion tag for each gathered article is positive or negative. Two factors are counted in this research: the number of tweets and the ratio of positive tweets for each candidate player. According to experimental result, the positive tweets has direct ratio with the number of votes in the NBA All-Star Game, a result suggesting that sentiment analysis is an effective tool for predicting human voting outcomes.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b795cb655cbed36b24ace6c4197c8b8e https://doi.org/10.1007/978-3-319-01796-9_20 Zobrazit plný text záznamu